Distance-to-fault power outage notification

ABSTRACT

Systems and methods comprising a metering device located on an electricity distribution grid, the metering device comprising one or more processors and memory. The metering device can detect a drop in characteristic of electricity below a threshold indicating a fault on the electricity distribution grid. The metering device can generate, responsive to the drop in the characteristic of electricity below the threshold, a time series of a rate of change of the characteristic of electricity for a predetermined number of cycles subsequent to the detection of the drop. The metering device can determine, based on a comparison of the time series of the rate of change with a predetermined pattern, a location of the metering device on the electricity distribution grid relative to a location of the fault on the electricity distribution grid.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 119 toU.S. Provisional Patent Application No. 63/210,625, filed Jun. 15, 2021,which is hereby incorporated by reference herein in its entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to systems and methods for detectingoutages within a utility grid. In particular, the systems and methods ofthis disclosure can locate the epicenter of the outage among meteringdevices that lost power.

BACKGROUND

Utility distribution grids can use meters to observe or measure utilitydelivery or consumption in the grid. These meters, among othercomponents within utility distribution grids can experience poweroutages for various reasons. Power outages in utility distribution gridsnegatively affect their reliability scores (e.g., the Customer AverageInterruption Duration Index (“CAIDI”)). However, it can be challengingto efficiently and accurately locate and repair faults when they occur.

BRIEF SUMMARY OF THE DISCLOSURE

Systems and methods of this disclosure are directed to locating a faultwithin a utility grid. Some utility grids may wait for their customersto report a power outage within their area, either via an online webportal or application. In some cases, the utility grids use phone callsfrom customers to identify an outage. Some other utility grids may usean advance metering infrastructure (“AMI”) as a “last gasp” or “dyinggasp” messaging system to receive a reporting of electrical outages. Forexample, when a metering device (e.g., customer metering device) sensesor detects that the voltage has dropped below a predetermined (or set)threshold for a predetermined amount of time (e.g., set by an operatorof the utility grid or the metering device), the metering device cantransmit a message (sometimes referred to as a gasp, last gasp, dyinggasp, or Power Outage Notification (“PON”)) to the utility OutageManagement System (“OMS”). The message can include the metering deviceidentifier (“ID”) and a timestamp indicating a time when the voltage ofthe metering device dropped below the predetermined threshold. Bytransmitting PON, the OMS may identify or map which metering devicesexperienced the outage. Being able to locate the closest metering deviceto a fault could greatly decrease the time spent searching for a faultthereby increasing reliability and decreasing customer downtime.However, it may be challenging to determine the epicenter or the sourceof the power outages within the utility grid with only the use of PON,hence, delaying the time to repair physical issues or restore powerwithin the utility grid.

The systems and methods of this disclosure can include a data processingsystem configured to determine the location of at least one fault (e.g.,electrical fault or outage) using information or signals received frommetering devices on the grid. For example, the metering devices cansample an analog signal, such as a characteristic of electricity (e.g.,voltage or current), at a sample rate (e.g., 500 Hz, 1 KHz, 1.5 Khz, 2Khz, or more). Each metering device can compute at least one respectivevalue associated with the characteristic of electricity during anoutage, such as a rate of change when the electrical characteristic isless than or equal to a threshold (e.g., PON threshold). In some cases,the metering device may generate and transmit a time series includingvalues of the rate of change to the data processing system. The dataprocessing system can plot the values (e.g., rate of changes) fromindividual metering devices on a map to generate a time series (e.g., atleast one value from each metering device). The time series can indicatethe rate of change of the characteristic of electricity from themetering devices. The data processing system can compare thecharacteristics (e.g., slopes) of the time series with patterns (orbased on the time series itself) to determine if each metering device isupstream of, downstream of, or relatively near the fault location, basedon similarities or matches between the characteristics and one or morepatterns. For example, a first set of patterns can include slopesrepresenting that the metering device is upstream, and a second set ofpatterns can include slopes representing that the metering device isdownstream. These patterns can be generated based on historical data.These patterns may be generated based on physics-inferredcharacteristics of electricity, for example, of various metering devicesduring an outage or fault. If the time series include characteristicssimilar to the pattern of the first set, the data processing system candetermine that the metering device is located upstream of the fault. Ifthe time series include characteristics similar to the pattern of thesecond set, the data processing system can determine that the meteringdevice is located downstream of the fault.

As such, by utilizing the characteristics of electricities from thedistributed metering devices and comparing the characteristics to one ormore patterns, the data processing system can determine the relativedistance of the metering devices to the fault. Thus, the systems andmethods can provide a technical solution in locating the fault relativeto the metering device location, thereby reducing the time to restoreservices (e.g., electrical utilities or distribution), decreasing theCAIDI score, and improving customer satisfaction.

In one aspect, this disclosure is directed to a method for locating afault within a utility grid. The method can include detecting, by ametering device located on an electricity distribution grid, a drop incharacteristic of electricity below a threshold indicating a fault onthe electricity distribution grid. The method can include generating, bythe metering device responsive to the drop in the characteristic ofelectricity below the threshold, a time series of a rate of change ofthe characteristic of electricity for a predetermined number of cyclessubsequent to the detection of the drop. The method can includedetermining, by at least one of the metering device or one or moreprocessors in communication with the metering device, based on acomparison of the time series of the rate of change with a predeterminedpattern, a location of the metering device on the electricitydistribution grid relative to a location of the fault on the electricitydistribution grid.

The characteristic of electricity can correspond to a characteristic ofvoltage measured by the metering device. In some cases, thecharacteristic of electricity can correspond to a characteristic ofcurrent measured by the metering device. In some cases, the method caninclude triggering, by the metering device, responsive to the detecteddrop in the characteristic of electricity, a power outage notification(PON). The method can include generating, by the metering device,responsive to the triggered PON, the time series of the rate of changeof the characteristic of electricity for the predetermined number ofcycles.

The method can include determining, by the metering device, the rate ofchange of the characteristic of electricity based on a derivative intime of a root mean square (RMS) of one of a voltage signal or a currentsignal. In some cases, the predetermined pattern can include at least anincline slope and a decline slope. A declination of the decline slopecan be greater than an inclination of the incline slope. The method caninclude determining, by the metering device, that the location of themetering device is downstream a first subset of metering devices andupstream a second subset of metering devices based on the predeterminedpattern. The incline slope can be associated with the first subset ofmetering devices upstream of the location of the metering device. Thedecline slope can be associated with the second metering devicedownstream of the location of the metering device.

The electricity distribution grid can include a second metering devicelocated at a different location from the metering device. The method caninclude detecting, by the second metering device, a second drop in thecharacteristic of electricity below the threshold indicating the faulton the electricity distribution grid. The method can include generating,by the second metering device responsive to the second drop in thecharacteristic of electricity below the threshold, a second time seriesof a second rate of change of the characteristic of electricity for thepredetermined number of cycles subsequent to the detection of the seconddrop. The method can include determining, by the second metering device,based on a second comparison of the second time series of the secondrate of change with the predetermined pattern, a location of the secondmetering device on the electricity distribution grid relative to thelocation of the fault on the electricity distribution grid.

The method can include providing, by the metering device, via a network,an indication of the location of at least one of the second meteringdevice or the fault on the electricity distribution grid to a computingsystem comprising one or more processors coupled to memory. In somecases, the method can include receiving, by a computing system, aplurality of time series of rates of change generated by a plurality ofmetering devices located on the electricity distribution grid responsiveto drops in characteristics of electricity. The method can includenormalizing, by the computing system, values of the plurality of timeseries. The method can include determining, by the computing system,based on the normalized values of the plurality of time series, alikelihood of fault location at each of the plurality of meteringdevices. The method can include determining, by the computing system,that the fault is upstream of a first metering device and downstream ofa second metering device based on the likelihood.

In another aspect, this disclosure is directed to a system for locatinga fault within a utility grid. The system can include a metering devicelocated on an electricity distribution grid, the metering devicecomprising one or more processors and memory. The metering device candetect a drop in characteristic of electricity below a thresholdindicating a fault on the electricity distribution grid. The meteringdevice can generate, responsive to the drop in the characteristic ofelectricity below the threshold, a time series of a rate of change ofthe characteristic of electricity for a predetermined number of cyclessubsequent to the detection of the drop. The metering device candetermine, based on a comparison of the time series of the rate ofchange with a predetermined pattern, a location of the metering deviceon the electricity distribution grid relative to a location of the faulton the electricity distribution grid.

The characteristic of electricity can correspond to a characteristic ofvoltage measured by the metering device. In some cases, thecharacteristic of electricity can correspond to a characteristic ofcurrent measured by the metering device. In some cases, the meteringdevice can trigger, responsive to the detected drop in thecharacteristic of electricity, a power outage notification (PON). Themetering device can generate, responsive to the triggered PON, the timeseries of the rate of change of the characteristic of electricity forthe predetermined number of cycles.

The metering device can determine, the rate of change of thecharacteristic of electricity based on a derivative in time of a rootmean square (RMS) of one of a voltage signal or a current signal. Insome cases, the predetermined pattern can include at least an inclineslope and a decline slope, wherein a declination of the decline slope isgreater than an inclination of the incline slope. The metering devicecan determine, that the location of the metering device is downstream afirst subset of metering devices and upstream a second subset ofmetering devices based on the predetermined pattern, wherein the inclineslope is associated with the first subset of metering devices upstreamof the location of the metering device, and wherein the decline slope isassociated with the second metering device downstream of the location ofthe metering device.

The electricity distribution grid can include a second metering devicelocated at a different location from the metering device. The secondmetering device can detect a second drop in the characteristic ofelectricity below the threshold indicating the fault on the electricitydistribution grid. The second metering device can generate, responsiveto the second drop in the characteristic of electricity below thethreshold, a second time series of a second rate of change of thecharacteristic of electricity for the predetermined number of cyclessubsequent to the detection of the second drop. The second meteringdevice can determine, based on a second comparison of the second timeseries of the second rate of change with the predetermined pattern, alocation of the second metering device on the electricity distributiongrid relative to the location of the fault on the electricitydistribution grid.

In another aspect, this disclosure is directed to a non-transitorycomputer readable storage medium locating a fault within a utility grid.The non-transitory computer readable storage medium can storeinstructions that, when executed by the one or more processors of acomputing system, cause the one or more processors to receive aplurality of time series of rates of change generated by a plurality ofmetering devices located on an electricity distribution grid responsiveto drops in characteristics of electricity. The one or more processorscan normalize values of the plurality of time series. The one or moreprocessors can determine, based on the normalized values of theplurality of time series, a likelihood of fault location at each of theplurality of metering devices. The one or more processors can determinethat a fault is upstream of a first metering device and downstream of asecond metering device based on the likelihood. The one or moreprocessors can provide, responsive to the determination, an indicationof a location of the fault upstream of the first metering device anddownstream of the second metering device.

The one or more processors can provide the indication of the location ofthe fault to a device remote from the computing system to facilitaterepair of the fault.

These and other aspects and implementations are discussed in detailbelow. The foregoing information and the following detailed descriptioninclude illustrative examples of various aspects and implementations,and provide an overview or framework for understanding the nature andcharacter of the claimed aspects and implementations. The drawingsprovide illustration and a further understanding of the various aspectsand implementations, and are incorporated in and constitute a part ofthis specification.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying drawings are not intended to be drawn to scale. Likereference numbers and designations in the various drawings indicate likeelements having similar structure or functionality. For purposes ofclarity, not every component may be labeled in every drawing. In thedrawings:

FIG. 1 is a block diagram depicting an illustrative utility grid, inaccordance with an implementation;

FIG. 2 is a block diagram depicting a system for locating a fault withina utility grid, in accordance with an implementation;

FIG. 3 are graphs of example simulated fault characteristics inwaveforms and root mean square (RMS) signals, in accordance with animplementation;

FIG. 4 is a graph of example root mean square (“RMS”) voltage timederivative behavior in the presence of a three-phase fault, inaccordance with an implementation;

FIG. 5A is a graph of an example accurate single line-to-ground (“SLG”)fault localization on the phase the fault occurred on, in accordancewith an implementation;

FIG. 5B is a graph of an example accurate SLG fault localization amongall three phases, in accordance with an implementation;

FIG. 5C is a graph of example successful fault localization results forSLG and three-phase faults with and without an upstream circuit breakertripping, in accordance with an implementation;

FIG. 6 is an example flow diagram of a method for locating a faultwithin a utility grid, in accordance with an implementation; and

FIG. 7 is a block diagram illustrating an architecture for a computersystem that can be employed to implement elements of the systems andmethods described and illustrated herein, including, for example,aspects of the utility grid depicted in FIG. 1 , the systems depicted inFIG. 2, and the operations depicted in FIGS. 3-6 .

The features and advantages of the present solution will become moreapparent from the detailed description set forth below when taken inconjunction with the drawings, in which like reference charactersidentify corresponding elements throughout. Other features, aspects, andadvantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and implementations of, methods, apparatuses, and systems oflocating a fault within a utility grid. The various concepts introducedabove and discussed in greater detail below may be implemented in any ofnumerous ways.

A utility grid, or utility distribution system, can distributeelectricity. A system that manages the utility grid can locate theepicenter or the source of the fault or power outages relative tometering devices (e.g., meters, adapters, smart grid chips, or otherelectrical measuring devices) within the utility grid based onmeasurements from the metering devices, such as the analog voltagewaveforms. Each metering device can determine at least one rate ofchange when a predetermined condition is triggered, such as before, at,or after a PON is triggered. Each metering device may generate a timeseries including values the rate of changes during the drop inelectricity (e.g., from PON start to PON end). In some cases, the systemcan receive the measurements from individual metering devices andcompute the rate of changes (e.g., a respective value associated witheach metering device) based on the measurements. The system can plot thecomputed rate of changes from various metering devices (e.g., at leastone value associated with the time series from each metering device) ona map to generate a time series. The time series can includecharacteristics (e.g., slopes, patterns, behavior, or waveforms) ofelectricity during the outage. The system can compare the time series(or characteristics of the time series) to patterns (e.g., determinedbased on historical data of the metering devices during outages or basedon physics-inferred electrical characteristics calculated or determinedfor metering devices relative to the fault location) of electrical(e.g., voltage or current) behavior. Based on the comparison includingsimilarities to one or more patterns, the system can determine thelocations of the metering devices relative to a fault location (e.g.,the epicenter of the outage), such as upstream, downstream, orrelatively near the fault location. Thus, the system can locate theepicenter of the outage, such as located downstream from one or moremetering devices associated with the first pattern or upstream from oneor more metering devices associated with the second pattern, therebyreducing the time to restore services (e.g., electrical utilities ordistribution), decreasing the CAIDI score, and improving customersatisfaction from less interruption in the distribution services.

Referring now to FIG. 1 , an example utility distribution environment isshown. The utility distribution environment can include a utility grid100. The utility grid 100 can include an electricity distribution gridwith one or more devices, assets, or digital computational devices andsystems, such as a computing device 700 or a data processing system 202(e.g., in conjunction with FIG. 2 ). In brief overview, the utility grid100 includes a power source 101 that can be connected via a subsystemtransmission bus 102 and/or via substation transformer 104 to a voltageregulating transformer 106 a. The voltage regulating transformer 106 acan be controlled by voltage controller 108 with regulator interface110. Voltage regulating transformer 106 a can be optionally coupled onprimary distribution circuit 112 via optional distribution transformer114 to secondary utilization circuits 116 and to one or more electricalor electronic devices 119. Voltage regulating transformer 106 a caninclude multiple tap outputs 106 b with each tap output 106 b supplyingelectricity with a different voltage level. The utility grid 100 caninclude monitoring devices 118 a-118 n that can be coupled throughoptional potential transformers 120 a-120 n to secondary utilizationcircuits 116. The monitoring or metering devices 118 a-118 n can detect(e.g., continuously, periodically, based on a time interval, responsiveto an event or trigger) measurements and continuous voltage signals ofelectricity supplied to one or more electrical devices 119 connected tocircuit 112 or 116 from a power source 101 coupled to bus 102. A voltagecontroller 108 can receive, via a communication media 122, measurementsobtained by the metering devices 118 a-118 n, and use the measurementsto make a determination regarding a voltage tap settings, and provide anindication to regulator interface 110. The regulator interface cancommunicate with voltage regulating transformer 106 a to adjust anoutput tap level 106 b.

Still referring to FIG. 1 , and in further detail, the utility grid 100includes a power source 101. The power source 101 can include a powerplant such as an installation configured to generate electrical powerfor distribution. The power source 101 can include an engine or otherapparatus that generates electrical power. The power source 101 cancreate electrical power by converting power or energy from one state toanother state. In some embodiments, the power source 101 can be referredto or include a power plant, power station, generating station,powerhouse or generating plant. In some embodiments, the power source101 can include a generator, such as a rotating machine that convertsmechanical power into electrical power by creating relative motionbetween a magnetic field and a conductor. The power source 101 can useone or more energy source to turn the generator including, e.g., fossilfuels such as coal, oil, and natural gas, nuclear power, or cleanerrenewable sources such as solar, wind, wave and hydroelectric.

In some embodiments, the utility grid 100 includes one or moresubstation transmission bus 102. The substation transmission bus 102 caninclude or refer to transmission tower, such as a structure (e.g., asteel lattice tower, concrete, wood, etc.), that supports an overheadpower line used to distribute electricity from a power source 101 to asubstation 104 or distribution point 114. Transmission towers 102 can beused in high-voltage AC and DC systems, and come in a wide variety ofshapes and sizes. In an illustrative example, a transmission tower canrange in height from 15 to 55 metering devices or more. Transmissiontowers 102 can be of various types including, e.g., suspension,terminal, tension, and transposition. In some embodiments, the utilitygrid 100 can include underground power lines in addition to or insteadof transmission towers 102.

In some embodiments, the utility grid 100 includes a substation 104 orelectrical substation 104 or substation transformer 104. A substationcan be part of an electrical generation, transmission, and distributionsystem. In some embodiments, the substation 104 transform voltage fromhigh to low, or the reverse, or performs any of several other functionsto facilitate the distribution of electricity. In some embodiments, theutility grid 100 can include several substations 104 between the powerplant 101 and the consumer electoral devices 119 with electric powerflowing through them at different voltage levels.

The substations 104 can be remotely operated, supervised and controlled(e.g., via a supervisory control and data acquisition system or dataprocessing system 202). A substation can include one or moretransformers to change voltage levels between high transmission voltagesand lower distribution voltages, or at the interconnection of twodifferent transmission voltages.

The regulating transformer 106 can include: (1) a multi-tapautotransformer (single or three phase), which are used fordistribution; or (2) on-load tap changer (three phase transformer),which can be integrated into a substation transformer 104 and used forboth transmission and distribution. The illustrated system describedherein can be implemented as either a single-phase or three-phasedistribution system. The utility grid 100 can include an alternatingcurrent (AC) power distribution system and the term voltage can refer toan “RMS Voltage”, in some embodiments.

The utility grid 100 can include a distribution point 114 ordistribution transformer 114, which can refer to an electric powerdistribution system. In some embodiments, the distribution point 114 canbe a final or near final stage in the delivery of electric power. Forexample, the distribution point 114 can carry electricity from thetransmission system (which can include one or more transmission towers102) to individual consumers 119. In some embodiments, the distributionsystem can include the substations 104 and connect to the transmissionsystem to lower the transmission voltage to medium voltage rangingbetween 2 kV and 35 kV with the use of transformers, for example.Primary distribution lines or circuit 112 carry this medium voltagepower to distribution transformers located near the customer's premises119. Distribution transformers can further lower the voltage to theutilization voltage of appliances and can feed several customers 119through secondary distribution lines or circuits 116 at this voltage.Commercial and residential customers 119 can be connected to thesecondary distribution lines through service drops. In some embodiments,customers demanding high load can be connected directly at the primarydistribution level or the sub-transmission level.

The utility grid 100 can include or couple to one or more consumer sites119. Consumer sites 119 can include, for example, a building, house,shopping mall, factory, office building, residential building,commercial building, stadium, movie theater, etc. The consumer sites 119can be configured to receive electricity from the distribution point 114via a power line (above ground or underground). A consumer site 119 canbe coupled to the distribution point 114 via a power line. The consumersite 119 can be further coupled to a site metering device 118 a-n oradvanced metering infrastructure (“AMI”). The site metering device 118a-n can be associated with a controllable primary circuit segment 112.The association can be stored as a pointer, link, field, data record, orother indicator in a data file in a database.

The utility grid 100 can include site metering devices 118 a-n or AMI.Site metering devices 118 a-n can measure, collect, and analyze energyusage, and communicate with metering devices such as electricity meters,gas meters, heat meters, and water meters, either on request or on aschedule. Site metering devices 118 a-n can include hardware, software,communications, consumer energy displays and controllers, customerassociated systems, Meter Data Management (MDM) software, or supplierbusiness systems. In some embodiments, the site metering devices 118 a-ncan obtain samples of electricity usage in real time or based on a timeinterval, and convey, transmit or otherwise provide the information. Insome embodiments, the information collected by the site metering device118 a-n can be referred to as meter observations or meteringobservations and can include the samples of electricity usage. In someembodiments, the site metering device 118 a-n can convey the meteringobservations along with additional information such as a uniqueidentifier of the site metering device 118 a-n, unique identifier of theconsumer, a time stamp, date stamp, temperature reading, humidityreading, ambient temperature reading, etc. In some embodiments, eachconsumer site 119 (or electronic device) can include or be coupled to acorresponding site metering device or monitoring device 118 a-118 n.

Monitoring devices 118 a-118 n can be coupled through communicationsmedia 122 a-122 n to voltage controller 108. Voltage controller 108 cancompute (e.g., discrete-time, continuously or based on a time intervalor responsive to a condition/event) values for electricity thatfacilitates regulating or controlling electricity supplied or providedvia the utility grid. For example, the voltage controller 108 cancompute estimated deviant voltage levels that the supplied electricity(e.g., supplied from power source 101) will not drop below or exceed asa result of varying electrical consumption by the one or more electricaldevices 119. The deviant voltage levels can be computed based on apredetermined confidence level and the detected measurements. Voltagecontroller 108 can include a voltage signal processing circuit 126 thatreceives sampled signals from metering devices 118 a-118 n. Meteringdevices 118 a-118 n can process and sample the voltage signals such thatthe sampled voltage signals are sampled as a time series (e.g., uniformtime series free of spectral aliases or non-uniform time series).

Voltage signal processing circuit 126 can receive signals viacommunications media 122 a-n from metering devices 118 a-n, process thesignals, and feed them to voltage adjustment decision processor circuit128. Although the term “circuit” is used in this description, the termis not meant to limit this disclosure to a particular type of hardwareor design, and other terms known generally known such as the term“element”, “hardware”, “device” or “apparatus” could be usedsynonymously with or in place of term “circuit” and can perform the samefunction. For example, in some embodiments the functionality can becarried out using one or more digital processors, e.g., implementing oneor more digital signal processing algorithms. Adjustment decisionprocessor circuit 128 can determine a voltage location with respect to adefined decision boundary and set the tap position and settings inresponse to the determined location. For example, the adjustmentdecision processing circuit 128 in voltage controller 108 can compute adeviant voltage level that is used to adjust the voltage level output ofelectricity supplied to the electrical device. Thus, one of the multipletap settings of regulating transformer 106 can be continuously selectedby voltage controller 108 via regulator interface 110 to supplyelectricity to the one or more electrical devices based on the computeddeviant voltage level. The voltage controller 108 can also receiveinformation about voltage regulator transformer 106 a or output tapsettings 106 b via the regulator interface 110. Regulator interface 110can include a processor controlled circuit for selecting one of themultiple tap settings in voltage regulating transformer 106 in responseto an indication signal from voltage controller 108. As the computeddeviant voltage level changes, other tap settings 106 b (or settings) ofregulating transformer 106 a are selected by voltage controller 108 tochange the voltage level of the electricity supplied to the one or moreelectrical devices 119.

The network 140 can be connected via wired or wireless links. Wiredlinks can include Digital Subscriber Line (DSL), coaxial cable lines, oroptical fiber lines. The wireless links can include BLUETOOTH, Wi-Fi,Worldwide Interoperability for Microwave Access (WiMAX), an infraredchannel or satellite band. The wireless links can also include anycellular network standards used to communicate among mobile devices,including standards that qualify as 1G, 2G, 3G, or 4G. The networkstandards can qualify as one or more generation of mobiletelecommunication standards by fulfilling a specification or standardssuch as the specifications maintained by International TelecommunicationUnion. The 3G standards, for example, can correspond to theInternational Mobile Telecommunications-2000 (IMT-2000) specification,and the 4G standards can correspond to the International MobileTelecommunications Advanced (IMT-Advanced) specification. Examples ofcellular network standards include AMPS, GSM, GPRS, UMTS, LTE, LTEAdvanced, Mobile WiMAX, and WiMAX-Advanced. Cellular network standardscan use various channel access methods e.g. FDMA, TDMA, CDMA, or SDMA.In some embodiments, different types of data can be transmitted viadifferent links and standards. In other embodiments, the same types ofdata can be transmitted via different links and standards.

The network 140 can be any type and/or form of network. The geographicalscope of the network 140 can vary widely and the network 140 can be abody area network (BAN), a personal area network (PAN), a local-areanetwork (LAN), e.g. Intranet, a metropolitan area network (MAN), a widearea network (WAN), or the Internet. The topology of the network 140 canbe of any form and can include, e.g., any of the following:point-to-point, bus, star, ring, mesh, or tree. The network 140 can bean overlay network which is virtual and sits on top of one or morelayers of other networks 140. The network 140 can be of any such networktopology as known to those ordinarily skilled in the art capable ofsupporting the operations described herein. The network 140 can utilizedifferent techniques and layers or stacks of protocols, including, e.g.,the Ethernet protocol, the internet protocol suite (TCP/IP), the ATM(Asynchronous Transfer Mode) technique, the SONET (Synchronous OpticalNetworking) protocol, or the SDH (Synchronous Digital Hierarchy)protocol. The TCP/IP internet protocol suite can include applicationlayer, transport layer, internet layer (including, e.g., IPv6), or thelink layer. The network 140 can be a type of a broadcast network, atelecommunications network, a data communication network, or a computernetwork.

One or more components, assets, or devices of utility grid 100 cancommunicate via network 140. The utility grid 100 can use one or morenetworks, such as public or private networks. The utility grid 100 cancommunicate or interface with a data processing system 202 designed andconstructed to communicate, interface or control the utility grid 100via network 140. Each asset, device, or component of utility grid 100can include one or more computing devices 700 or a portion of computingdevice 700 or some or all functionality of computing device 700.

Referring to FIG. 2 , a block diagram illustrating an example system tolocate a fault within a utility grid is shown. The system 200 caninclude, interface with, access, or otherwise communicate with at leastone utility grid 100, at least one data processing system 202, or atleast one server 204. The data processing system 202 can include one ormore components (e.g., one or more processors, memory, databases,interfaces, etc.) configured to perform features or functionalitiesdiscussed herein for managing the utility grid 100 or locating one ormore faults or epicenter of power outages within the utility grid 100.The data processing system 202 can correspond to a metering devicelocated in the utility grid 100. In some cases, the data processingsystem 202 can be a computing device remote from the utility grid 100 orthe server 204. In some other cases, the data processing system 202 canbe another server different from the server 204, configured to performfeatures and functionalities to manage the utility grid 100. The dataprocessing system 202 can transmit or receive data to or from othercomponents (e.g., utility grid 100 or server 204) of the system 200 viathe network 140. The utility grid 100 and the network 140 can bereferred to in conjunction with FIG. 1 . The one or more devices,components, or systems (e.g., data processing system 202, server 204,metering devices 118, etc.) of the utility grid 100 or the system 200can be composed of hardware, software, or a combination of hardware andsoftware components.

The server 204 can be implemented with hardware, software, or acombination of hardware and software components. The server 204 can be aremote computing device or remote processing component from the dataprocessing system 202 and the utility grid 100. In some cases, theserver 204 can include or be a cloud storage device, such as configuredto store data from the data processing system 202. For example, theserver 204 can receive data from the data processing system 202, such asmeasured or processed data of the utility grid 100. The server 204 maystore these data for retrieval by the data processing system 202. Insome cases, the server 204 can include one or more processing logics orcomponents to perform one or more tasks according to instructionsreceived from the data processing system 202. For example, the server204 can receive a delegated tasks or objectives from the data processingsystem 202. The server 204 can process data receive from the dataprocessing system 202 and transmit the processed data back to the dataprocessing system 202. In some cases, the server 204 can perform otherfeatures or functionalities of the data processing system 202.

The network 140 can couple devices, components, or systems forcommunication, such as the utility grid 100, data processing system 202,and server 204. For example, devices or systems (e.g., data processingsystem 202, server 204, utility grid 100, etc.) within the system 200can communicate or interchange information via the network 140.

The data processing system 202 can include or correspond to at least onemetering device 118, such as one of the metering devices 118 configuredto perform one or more features (e.g., collect and process electricitycharacteristics) to triangulate a location of a fault in the utilitygrid 100. The data processing system 202 can reside on a metering deviceor a computing device of the utility grid 100. In some cases, the dataprocessing system 202 can reside on a computing device or serverexternal or remote from the utility grid 100. For instance, the dataprocessing system 202 can reside or execute in a cloud computingenvironment or distributed computing environment. The data processingsystem 202 can reside on or execute on multiple local computing deviceslocated throughout the utility grid 100. For example, the utility grid100 can include multiple local computing devices each configured withone or more components or functionality of the data processing system202.

The data processing system 202 can include one or more components tolocate or determine the location of a fault on the utility grid 100(e.g., electricity distribution grid), for instance, at least oneinterface 206, at least one electricity detector 208, at least one timeseries generator 210, at least one meter locator 212, and at least onedatabase 214. Each of the components (e.g., interface 206, electricitydetector 208, time series generator 210, meter locator 212, or database214) of the data processing system 202 can be implemented using hardwareor a combination of software and hardware. Each component of the dataprocessing system 202 can include logical circuitry (e.g., a centralprocessing unit or CPU) that responses to and processes instructionsfetched from a memory unit (e.g., memory 715 or storage device 725).Each component of the data processing system 202 can include or use amicroprocessor or a multi-core processor. A multi-core processor caninclude two or more processing units on a single computing component.Each component of the data processing system 202 can be based on any ofthese processors, or any other processor capable of operating asdescribed herein. Each processor can utilize instruction levelparallelism, thread level parallelism, different levels of cache, etc.For example, the data processing system 202 can include at least onelogic device such as a computing device or server having at least oneprocessor to communicate via the network 140.

The components and elements (e.g., interface 206, electricity detector208, time series generator 210, meter locator 212, or database 214) ofthe data processing system 202 can be separate components, a singlecomponent, or part of the data processing system 202. For example,individual components or elements of the data processing system 202 canoperate concurrently to perform at least one feature or functiondiscussed herein. In another example, components of the data processingsystem 202 can execute individual instructions or tasks. In yet anotherexample, the components of the data processing system 202 can be asingle component to perform one or more features or functions discussedherein. The components of the data processing system 202 can beconnected or communicatively coupled to one another, such as via theinterface 206. The connection between the various components of the dataprocessing system 202 can be wired or wireless, or any combinationthereof. Counterpart systems or components can be hosted on othercomputing devices.

The interface 206 can interface with the network 140, devices within thesystem 200 (e.g., server 204 or utility grid 100), or components of thedata processing system 202. The interface 206 can include features andfunctionalities similar to the communication interface of one or moremetering devices 118 to interface with the aforementioned components,such as in conjunction with FIG. 1 . For example, the interface 206 caninclude standard telephone lines LAN or WAN links (e.g., 802.11, T1, T3,Gigabit Ethernet, Infiniband), broadband connections (e.g., ISDN, FrameRelay, ATM, Gigabit Ethernet, Ethernet-over-SONET, ADSL, VDSL, BPON,GPON, fiber optical including FiOS), wireless connections, or somecombination of any or all of the above. Connections can be establishedusing a variety of communication protocols (e.g., TCP/IP, Ethernet,ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), IEEE802.11a/b/g/n/ac CDMA, GSM, WiMax and direct asynchronous connections).The interface 206 can include at least a built-in network adapter,network interface card, PCMCIA network card, EXPRESSCARD network card,card bus network adapter, wireless network adapter, USB network adapter,modem, or any other device suitable for interfacing one or more deviceswithin the system 200 to any type of network capable of communication.The interface 206 can communicate with one or more aforementionedcomponents to receive data from at least one of the server 204 or theutility grid 100, such as data representative of electricitydistribution to individual metering devices 118 within the utility grid100.

The data processing system 202 can obtain measurements from two or moremetering devices within the utility grid 100. The metering devices caninclude or refer to at least two of the metering devices 118. The dataprocessing system 202 may obtain measurements from the server 204. Forinstance, the metering device 118 may send measurements or data to theserver 204 for storage or processing. In this case, the data processingsystem 202 can access or obtain the measurements or processed data fromthe server 204 collected from one or more metering devices 118. In somecases, the data processing system 202 can correspond to at least one ofthe metering devices 118. For example, at least one component (e.g.,electricity detector 208) of the data processing system 202 can receiveelectrical signals or power from the substation 104 (or the distributionpoint 114) to perform a measurement (e.g., one or more features of themeasurement circuits of the metering device 118).

The electricity detector 208 can measure or detect an analog voltagewaveform at high sampling rates (e.g., 0.5 KHz, 1 KHz, 2 KHz, 3 KHz, 6kHz or higher) in order to accurately resolve them in digitized samples.In some cases, the electricity detector 208 can measure analog currentwaveform at the sampling rates (e.g., 1 kHz or higher). The electricitydetector 208 can measure the analog waveform (e.g., voltage or currentwaveform) at a particular location on the utility grid 100 (e.g., alocation of a respective metering device 118 or the data processingsystem 202). The analog waveform can represent the voltage, current, orpower supplied by at least one of the power source 101 or the substation104. Other data processing systems or metering devices 118 can performsimilar features or functionalities discussed herein, such astriangulating a location of a fault in the utility grid 100 with respectto the various metering devices 118 experiencing an outage.

The electricity detector 208 can determine a root mean square (RMS)signal from the analog waveform, such as RMS voltage signal, RMS currentsignal, or RMS power signal. For purposes of providing examples, voltagewaveform or RMS voltage signal can be used to detect an outage andlocate a fault, however, other electrical waveforms or signals (e.g.,current, power, etc.) can be measured and used to perform similarfeatures. For example, the electricity detector 208 can determine theRMS voltage signal based on at least one of the peak, peak-to-peak, oraverage of the voltage waveform. The electricity detector 208 candetermine the RMS signal half cycle from the measured waveform (e.g.,the RMS signal may lag the waveform by half a cycle). In some cases, theelectricity detector 208 can calculate the RMS signal greater than orless than half a cycle of the corresponding waveform.

Based on the measurements, the electricity detector 208 can determine acharacteristic of electricity in the utility grid 100. For example, theRMS signal can correspond to or represent a characteristic ofelectricity measured, detected, determined, or otherwise characterizedby the electricity detector 208. In some cases, the analog waveform cancorrespond to or represent the characteristic of electricity detected bythe electricity detector 208. The characteristic of electricity caninclude at least one of stable (e.g., constant Vrms), fluctuations,increases, decreases, drops, or other patterns or behaviors. Thecharacteristic of electricity can change based on electricitydistributed from the substation 104 or due to interruptions at or inbetween one or more metering devices 118 (or data processing systems).In some cases, the characteristic of electricity can be based on whethera breaker is tripped at the substation 104.

The electricity detector 208 can detect a drop in the characteristic ofelectricity (e.g., RMS signal) below a threshold, such as based on acomparison between the signal (e.g., Vrms, Irms, Prms, etc.) and thethreshold. The threshold may include, correspond to, or be referred toas a drop threshold, a characteristic threshold, or a minimumelectricity threshold. The threshold may be configured or set by theadministrator of the data processing system 202 or the manufacturer ofthe metering devices 118. The threshold may be in percentage or valuewith respect to the average, maximum, or expected amplitude (e.g.,amount) of electricity supplied to the metering device 118 or entities(e.g., buildings, etc.). For example, the threshold may be set to 90%,85%, 80%, etc. In another example, if the average or expectedelectricity supplied to the metering devices 118 is 120 Vrms, thethreshold may be set to 108 Vrms, 102 Vrms, 96 Vrms, etc. In this case,the threshold may be with respect to the electricity supplied toindividual metering devices 118. In some cases, different meteringdevices 118 can be supplied with different amplitudes (e.g., 170 Vrms,150 Vrms, etc.).

The electricity detector 208 may continuously (e.g., at the sample rate)determine the RMS signal responsive to measuring, detecting, orobtaining the waveform, such as to determine the drop in electricity(e.g., characteristic in electricity). In some cases, the electricitydetector 208 may calculate the RMS responsive to a drop in voltage belowthe threshold using the waveform. For example, the electricity detector208 can determine that at least one of the peak, peak-to-peak, oraverage voltage drop below a predetermined threshold (e.g., amplitude,percentage, minimum value, etc.). The electricity detector 208 candetermine the Vrms of the corresponding waveform responsive to the dropin electricity. The electricity detector 208 can determine the Vrmsprior to, at, or subsequent to a time period when the voltage of thewaveform drops below the threshold, for example.

The electricity detector 208 may set or trigger a power outagenotification (PON) timer responsive to the electrical drop (e.g., thedrop in electricity or characteristic of electricity) below thethreshold. The PON timer can initiate a countdown or a timer from a timeat which the electricity drops below the threshold. For example, the PONtimer may be set or preconfigured to 0.2 ms, 0.1 ms, 0.05 ms, 0.025 ms,etc. The PON timer may stop or reset in response to the electricity(e.g., voltage) returning above the threshold. If the electrical signaldoes not return or increase above the threshold (e.g., maintain orsustain an RMS below the threshold), the electricity detector 208 candetect a timeout based on or from the PON timer. The PON timer can bepreconfigured based on standards (e.g., PON standards, meter standards,etc.), by the manufacturer of the metering devices, or by theadministrator of the data processing system 202.

Responsive to the timeout, the electricity detector 208 can trigger aPON sending process. For example, the electricity detector 208 can senda PON to the server 204, a computing device of the utility grid 100, asecond data processing system, at least one other metering device, orother devices connected to the network 140 in response to the timeout ofthe PON timer. In some cases, the electricity detector 208 can receivePONs from various other metering devices, such as a subset of meteringdevices located in the utility grid 100 that experiences the outage. ThePON can include at least an identifier (e.g., metering device ID) of therespective metering device and one or more timestamps. The timestamp canbe associated with at least one of a PON start (e.g., the start of thePON timer), PON end (e.g., timeout of the PON timer), triggering of PONsending process, among others. The metering device ID can be used toobtain information associated with the respective metering device, suchas the location, measured waveform, computed RMS signal, generated timeseries, etc. For instance, the electricity detector 208 (e.g., or timeseries generator 210 or meter locator 212) can access a table includinga list of metering devices (e.g., metering device IDs) storing theassociated metering device information, such as the location,electricity measurement data, etc. associated with a metering device ID.Hence, the electricity detector 208, or other components (e.g., timeseries generator 210 or meter locator 212) of the data processing system202 can retrieve or obtain characteristics of electricity measured orcomputed by various metering devices located in the utility grid 100experiencing the outage. In some cases, the table including the listingof metering devices and associated information may be stored on theserver 204. In some cases, the table can be stored in the database 214,such as local to the data processing system 202 or local to individualmetering devices 118. In some cases, the one or more metering devicescan send information including the characteristic of electricity with oras part of the PON.

In some cases, the detected or measured waveforms can vary incharacteristics before or during the outage, such as based on the typeof power. For example, the electricity detector 208 can measure ordetect waveforms for a single-phase, 3-phase power (e.g., phase A, phaseB, and phase C), etc. The power lines connecting the metering deviceswithin a zone (e.g., area or locations of metering devices) or under thesame substation 104 may supply a similar type of power (e.g.,single-phase or 3-phase power). In some cases, certain metering devicesmay receive a type of power different from other metering devices. Forinstance, a first metering device may measure a waveform forsingle-phase power, while a second metering device may measure anotherwaveform for 3-phase power. For single-phase power, the electricitydetector 208 may measure a single waveform over time. For three-phasepower, the electricity detector 208 may measure or detect threewaveforms associated with the three phases. The electricity detector208, time series generator 210, or the meter locator 212 may analyze oneor multiple phases as discussed herein.

For example, based on a pre-configuration by the administrator oroperator of the metering device, utility grid 100, or the dataprocessing system 202, the electricity detector 208 may determine theRMS signal for one of the phases or multiple phases measured by themetering devices for generating the time series. The electricitydetector 208 can determine which of the phases to analyze based on whichof the phases faulted, for instance, the phrase that caused the drop inelectricity thereby triggering the PON sending process. In some cases,the electricity detector 208 may determine to analyze multiple faultedphases. Accordingly, the electricity detector 208 can determine the RMSsignal for one or more phases of the measured waveform to provide thetime series generator 210 for generating at least one time series.

The time series generator 210 can generate a time series of a rate ofchange of the characteristic of electricity for a predetermined numberof cycles subsequent to the detection of the drop. The time seriesgenerator 210 can generate the time series in response to detecting thedrop in the characteristic of electricity (e.g., the RMS signal lowerthan the threshold) for one or more metering devices. In some cases, thetime series generator 210 may generate the time series in response toreceiving PONs from metering devices within the utility grid 100.Receiving the PONs may correspond to or include accumulating orobtaining at least RMS signals of a subset of metering devices sendingthe PONs, timestamps (e.g., PON start, PON end, etc.), metering deviceIDs, location of metering devices sending the PONs, etc. For example,the time series generator 210 can monitor a count of the number ofactive metering devices and the number of PONs received responsive to anoutage. The time series generator 210 may proceed to generate the timeseries in response to obtaining a predetermined portion of PONs (e.g.,including RMS signals and timestamps of at least the PON start) frommetering devices experiencing the outage, such as 50%, 70%, 85%, 90%,95%, etc. of the total number of meters.

In some cases, the time series generator 210 may initiate the timeseries generation process in response to receiving at least apredetermined number of PONs, such as 10, 20, 50, etc., where individualPONs may be associated with respective meters. In some cases, at leastone metering device may send multiple PONs, such as when the electricityfluctuates above and below the threshold subsequent to trigging the PONsend process. In another example, the time series generator 210 mayreceive a notification or be triggered by the electricity detector 208subsequent to triggering the PON (e.g., PON sending process) to generatethe time series.

In some cases, the time series generator 210 may initiate the timeseries generation process at a predetermined time subsequent toreceiving or detecting a PON (e.g., a first PON). For example, thepredetermined time may be any length of time to provide various meteringdevices (e.g., 80%, 90%, etc. of the total number of metering devices)experiencing the outage to send the PON, such as 0.1 seconds, 0.05seconds, etc. Accordingly, the time series generator 210 may standby forthe predetermined time after receiving the first PON (e.g., and thecharacteristic of electricity for one or more metering devices does notrise above the threshold) to collect data for generating the timeseries.

To generate a time series, the time series generator 210 can determine aderivative (e.g., dV/dt, &Mt, etc.) in time of the RMS signal (e.g., RMSvoltage signal, RMS current signal, etc.). The derivative of the signalcan represent the rate of change in the RMS signal at a particular pointin time. For instance, higher dV/dt corresponds to a greater rate ofchange and lower dV/dt corresponds to a lower rate of change ofelectricity (e.g., rate of the drop in electricity for a particularmetering device). The time series generator 210 can compute or determinethe derivative of the RMS signal at a period of time (e.g., a point intime of the signal) during the electrical drop. The time at which todetermine the derivative can be preconfigured by the administrator ofthe metering devices or the data processing system 202 based on an event(e.g., PON start, among others), such as at, before, or after the PONstart. The PON start time can vary between metering devices, withdifferent rates of change. For purposes of example, the time seriesgenerator 210 can determine the derivative herein for one or moremetering devices at the PON start timestamp associated with respectivemeters. In some other cases, the time series generator 210 can determinethe derivative at another point in time (e.g., before or after the PONstart) during the electricity drop.

The time series generator 210 can generate the derivative for variousmetering devices that sent the PONs. In some cases, individual meteringdevices can include one or more features or functionalities of the dataprocessing system 202, such that the time series generator 210 canreceive at least one of the computed derivative or generated time seriesfrom other metering devices (e.g., to update an existing time series).

The time series generator 210 can generate a time series of values basedon derivatives of RMS signals from metering devices 118 that detect thedrop in electricity or send the PONs. The time series can be used togenerate a plot of metering devices and the dV/dt (or dI/dt, etc.)associated with the respective meters. The time series generator 210 caninclude the metering devices and the dV/dt values on one of the axes ofthe plot, such as metering devices on the x-axis and dV/dt on they-axis, or vice versa. The time series generator 210 can arrange themetering devices based on the location (e.g., obtained from the server204 or the database 214). For example, the time series generator 210 canarrange the listing of metering devices in the time series from closestto furthest from the power source 101, substation 104, as discussed inexamples herein. In some cases, the time series generator 210 mayarrange the metering devices from the furthest to closest to thesubstation 104. Accordingly, based on the location of the meteringdevices and the determined derivatives, the time series generator 210can generate the time series for comparing metrics (e.g., dV/dt)computed between metering devices that sent the PON to determine orestimate a distance to the fault.

In some cases, the time series generator 210 may generate the timeseries for one or more phases, such as for 3-phase power. For example,the time series generator 210 can identify at least one faulted phasethat triggered PON. The time series generator 210 may compute thederivative (e.g., metric) of the RMS signal associated with the at leastone faulted phase for various meters. Accordingly, the time seriesgenerator 210 can generate the time series based on the derivative ofthe at least one phase measured by the meters. In some cases, the timeseries generator 210 may generate multiple time series for variousphases measured by the individual meters.

The time series generator 210 can normalize the time series, forexample, to 100, among other values. The normalization of the timeseries can represent a likelihood percentage for the fault location tocorrespond to or be near the location of the respective meter. Forexample, a first metering device, a second metering device, and a thirdmetering device can be located in the electricity distribution grid. Thetime series generator 210 can determine and plot the dV/dt of the threemetering devices in the time series. For example, a first dV/dt of thefirst metering device may be 10 Vrms/μs, a second dV/dt of the secondmetering device may be 5 Vrms/μs, and the third dV/dt of the thirdmetering device may be 2 Vrms/μs. When normalizing to 100, for example,the time series generator 210 can determine a multiplier (e.g.,normalization factor or scaling value) for all derivatives by dividing100 with the highest computed derivative (e.g., 10 in this case). Inthis example, the time series generator 210 can determine that themultiplier is 100/10=10 for normalizing the derivatives. Accordingly,the dV/dt of the first metering device is normalized to 100, the secondmetering device is normalized to 50, and the third metering device isnormalized to 20. The time series generator 210 can perform otherfeatures, functionalities, or techniques to normalize the time series(e.g., values of the time series). In some cases, the time seriesgenerator 210 can use the normalized derivatives to generate the timeseries.

The meter locator 212 can determine the location of one or more meteringdevices relative to the fault on the utility grid 100 based on the timeseries. The meter locator 212 can use the generated time series tocompare the metrics (e.g., dV/dt) between the metering devices todetermine the locations of the metering devices relative to the fault.The comparison between the metering devices can refer to or includecomparing the magnitude of the computed derivatives between the meteringdevices, such as to determine one or more metering devices with thehighest rate of change (e.g., dV/dt) of the characteristic ofelectricity at the PON start time period or between the PON start andPON end timeframe.

In some cases, the comparison can include or correspond to comparing thetime series to one or more predetermined patterns. For example, themeter locator 212 may compare the time series to at least onepredetermined pattern stored in the database 214 or obtained from anexternal device (e.g., the server 204 or a computing device of theutility grid 100). The meter locator 212 can compare or match the timeseries to one or more patterns to identify a relative location of thefault. The patterns can represent or include historical derivative dataof various metering devices measured, computed, or recorded during anoutage or fault. The one or more patterns can be associated with atleast one of a phase A fault, phase B fault, phase C fault, acombination of multiple faulted phases, single line-to-ground (SLG)fault, double SLG fault, line-to-line-to-ground fault, boltedline-to-line fault, three-phase bolted fault, among others. The one ormore patterns can be associated with whether a circuit breaker locatedat the substation 104 is tripped. For instance, the meter locator 212can compare the series to a specific pattern based on the type of faultindicated in the PON (e.g., the metering device detecting the type offault) or whether the breaker was tripped at the substation 104. Themeter locator 212 can obtain information on the type of fault and thebreaker trip from at least one of the individual metering devicessending PONs, the computing device of the utility grid 100 or associatedwith the substation 104 (e.g., monitoring electricity or equipmentevents), the server 204, among other devices.

In some cases, the meter locator 212 may compare the time series to oneor more patterns to identify at least one pattern with the highestsimilarities. For example, the meter locator 212 can identify at leastone pattern with at least one of a similar rate of inclination,declination, sustention, among other characteristics to the time series.The pattern may include a relatively inclined portion and a relativelydeclined portion, or other subsets of patterns. Upon identifying thecomparable pattern to the time series, the meter locator 212 mayidentify or determine the relative location of the fault in the timeseries (e.g., relative to the location of the data processing system 202or at least one metering device 118) based on an indication of ahistorical fault identified in the pattern.

For example, the pattern may indicate a location of the fault based onthe characteristics of the drop in electricity (e.g., patterns of thevoltage waveform drop, such as shown in FIG. 3 ). The pattern can bebased on historical measurement data from various metering deviceswithin the grid 100 during past outages. The pattern can be managed,edited, or configured by the administrator of the data processing system202, for example. In some cases, the pattern can indicate a faultlocation at a peak (e.g., highest point or max value) of the normalizedtime series. The peak may be a point of transitioning between aninclination (e.g., the incline slope) and declination (e.g., the declineslope). The pattern may be based on historical data, such as one or morepast generated or normalized time series during outages. The pattern maybe generated historically by the data processing system 202, one or moremetering devices within the utility grid 100, other data processingsystems, the server 204 (e.g., cloud computing device), among otherdevices. One or more examples of the fault location can be shown in atleast FIGS. 4-5C. In some cases, the pattern may be generated based onphysics-inferred characteristics of electricity (e.g., voltage orcurrent) during outages. For example, characteristics of electricityduring an outage for each metering device, such as the rate of change inelectricity, may be different based on at least one of the distance fromthe fault location or whether the metering device is upstream ordownstream from the fault location. According to the physics ofelectricity, the server 204 (or other devices within the network 140)can determine the expected characteristics of electricity (e.g., rate ofchange or measurement fluctuation) associated with various locationsupstream or downstream from a fault location. Hence, the pattern can begenerated based on the expected physics-inferred electricalcharacteristics, e.g., for the meter locator 212 to compare measurementsfrom the metering devices to the pattern.

The meter locator 212 can determine that the time series exhibitssimilar behaviors as one or more patterns representing a metering devicelocated upstream of a fault location. The meter locator 212 candetermine that the time series exhibits similar behaviors as one or morepatterns representing a metering device located downstream of the faultlocation. If the time series matches or is similar to at least onepattern, the meter locator 212 can use the fault location from thesimilar (e.g., comparable) pattern for mapping to the fault locationassociated with the metering devices in the time series (e.g.,normalized time series). For example, the pattern can indicate that thefault location is at the peak (e.g., highest derivative(s) or normalizedvalue(s)), near the peak (e.g., before or after the peak), at least oneof the peaks if the pattern and the time series include multiple peaks,among other portions of the time series. In some cases, the meterlocator 212 can compare multiple phases to multiple patterns todetermine a set of patterns that matches or is similar to the timeseries. For example, the meter locator 212 can identify a set ofpatterns (e.g., three patterns associated with phases A-C) havingsimilar behavior during the outage as the time series. The meter locator212 can compare a first pattern, a second pattern, and a third patternin the set of patterns to the computed derivatives associated withphases A-C, respectively. The patterns or behaviors of the individualphases may be shown in at least FIG. 5B, for example. The pattern caninclude fluctuations, steepness of inclinations or declinations, numberof peaks, sections of incline or decline, among other types of patternsto be compared to the time series or normalized time series.

If the time series is not similar to at least one pattern, the meterlocator 212 may be preconfigured to send the time series to theadministrator of the data processing system 202 or the computing deviceof the utility grid 100, for example. In some cases, if the time seriesdoes not match at least one pattern, the meter locator 212 may select oridentify the location(s) associated with one or more metering deviceswith the highest change in voltage at or around the PON start timeperiod as the metering devices nearest to the fault location. Forinstance, the meter locator 212 can identify one or more meteringdevices in the normalized time series representing the highestlikelihood of the fault location. Accordingly, the meter locator 212 canidentify one or more metering device locations (e.g., fitting thepattern or associated with the highest likelihood of being near or atthe fault location) to estimate or determine the approximate or relativelocation of the fault with respect to the one or more metering devices.

Responsive to identifying one or more metering devices nearest or at thefault location, the meter locator 212 can provide the location(s) of themetering device(s) for display or as an alert. For example, the meterlocator 212 can identify and provide the location of at least onemetering device to a display device or an external device (e.g., thecomputing device of the utility grid 100) operated by an administratoror operator. The administrator may access, view, or otherwise obtain oneor more metering device locations associated with at least one faultlocation to restore power to the metering devices 118 or entities (e.g.,buildings, etc.), such as by repairing or performing maintenance to thepower line, etc. Accordingly, by providing the fault location inresponse to the drop in the characteristic of electricity, the dataprocessing system 202 (e.g., components thereof) can improve theCustomer Average Interruption Duration Index (CAIDI) score or increasecustomer satisfaction utilizing the electricity from the utility grid100.

The data processing system 202 can include the database 214 to storeinformation or data collected, measured, obtained, or otherwise receivedas discussed herein. The database 214 may be referred to as a datastorage, data repository, memory device, etc. The database 214 caninclude at least a characteristic storage 216, time series storage 218,pattern storage 220, and metering device location storage 222. Thedatabase 214 can include other types of storage to store information forlocating the fault within the electricity distribution grid. In somecases, information stored in the database 214 can be uploaded to theserver 204, among other cloud storage devices, and downloaded to thedatabase 214 for processing. In some other cases, the information storedin the database 214 may be local to the data processing system 202. Thedatabase 214 can be accessed by one or more components (e.g.,electricity detector 208, time series generator 210, or meter locator212) of the data processing system 202, or at least one external device,such as other metering devices within the utility grid 100, the server204, etc.

The characteristic storage 216 can include, store, or maintain thecharacteristic of electricity measured by the electricity detector 208or the meter. The characteristic storage 216 can store thecharacteristic of electricity of other meters. The characteristicstorage 216 can include timestamps associated with the measuredelectricity. For example, the characteristic storage 216 cancontinuously store measurements from the electricity detector 208 or oneor more metering devices within the grid. The characteristic storage 216can include timestamps in ms, μs, etc. associated with the measurements.The characteristic of electricity can include or correspond to at leastone of the measured waveform or computed RMS signal.

The time series storage 218 can include, store, or maintain time seriesassociated with various meters. The time series may be generated by thetime series generator 210. For example, responsive to generating thetime series, the time series storage 218 can receive the time seriesfrom the time series generator 210 for storage. The time series storage218 may store the derivatives (e.g., dV/dt, dI/dt, etc.) of the RMSsignals associated with respective meters. For example, the time seriesgenerator 210 may compute the derivatives of the RMS signals and storethe computation results in the time series storage 218. The time seriesgenerator 210 may use the derivatives to generate the time series. Thetime series storage 218 can store the normalized time series, such asnormalizing the derivatives to 100, among other values. The time seriesstorage 218 may store a time series or plot of the likelihood of faultlocation, which may correspond to the normalized time series.

The pattern storage 220 can include, store, or maintain historicalpatterns of the characteristic of electricity (e.g., or time series ornormalized time series) during an outage. The patterns can be associatedwith a single phase power, 3-phase power, individual phases of the3-phase power, etc. The patterns can be historical patterns measured,analyzed, or constructed during an outage of at least one of the phases.The pattern storage 220 can store a fault location associated with therespective pattern. For example, the pattern storage 220 may store orindicate a fault location to be at a peak, before the peak, or after thepeak of the time series. The pattern storage 220 may include the rate ofinclination, rate of declination, the number of peaks, fluctuation rate,among other types of patterns. The pattern may be associated withwhether the breaker at the substation 104 has been tripped due to theoutage. The pattern storage 220 may be accessed by the meter locator212, such as to compare the pattern to the time series (or normalizedtime series). For instance, by comparing the pattern to the time series,the meter locator 212 can identify the fault location relative to themetering device locations based on a mapping to the fault locationindicated in the pattern. The one or more patterns may be updated, suchas by the administrator of the utility grid 100 or the component(s) ofthe data processing system 202 responsive to an actual location of thefault (e.g., determined by electricians repairing the power lines, etc.)relative to one or more metering devices. In some cases, the one or morepatterns may be generated by a machine learning engine trained usinghistorical time series and the reported fault location relative to themetering devices, for example.

The metering device location storage 222 can include, store, or maintainthe locations of the meters. The metering device location storage 222can store the metering device locations provided by the computing deviceof the utility grid 100 (e.g., or the administrator-provided location)or the server 204. The metering device location storage 222 may updatethe location of one or more metering devices based on metering device'savailability or changes in the location reported by the computing deviceof the utility grid 100 or the server 204. The metering device locationstorage 222 can include metering device IDs associated with the meteringdevice locations. In some cases, responsive to determining the faultlocation, the metering device location storage 222 may store or includethe location of the fault relative to individual metering devicelocations. For instance, the operator or electrician may retrieve atleast one metering device location and determine a distance, direction,or location from the fault. The metering device location storage 222 mayupdate the metering device location upon receiving an identification ofa new metering device added to the electricity distribution grid,removal of existing metering device(s), etc.

Referring now to FIG. 3 , depicted are graphs of example simulated faultcharacteristics 300 in waveforms and RMS signals. The simulated faultcharacteristics 300 can include a waveform graph 302 and RMS signalgraph 304. The one or more features or functionalities discussed hereinto generate or compute the graphs 302 and 304 (e.g., subplots) can beperformed by one or more devices, components, or systems (e.g., the dataprocessing system 202, server 204, network 140, metering devices 118, orcomputing device of the utility grid 100) of at least the system 200 inconjunction with FIG. 2 . For example, the data processing system 202can measure the characteristic of electricity (e.g., voltage, current,power, etc.) associated with one or more metering devices located in theelectricity distribution grid to generate or construct the waveformgraph 302. In some cases, the data processing system 202 can obtain datafrom various metering devices to generate or present the waveform graph302. In another example, using the measured waveform, the dataprocessing system 202 can compute or determine corresponding RMS valuesor signals. The data processing system 202 can generate and present theRMS signals as in RMS signal graph 304, for example.

In further example, by utilizing the digitized waveforms (e.g., of graph302) at high sampling rates or resolutions, the data processing system202 can perform computation on metering points that are experiencingpower outages to determine the characteristics of the power outages(e.g., drop in the characteristic of electricity). For example, the dataprocessing system 202 can compute the characteristics (e.g.,fluctuations, increases, decreases, among other patterns) in thebehavior of the voltage during the outage. In other examples, the dataprocessing system 202 may compute the characteristic in the behavior ofthe current or power during the outage. Based on the characteristics,the data processing system 202 can determine a relative distance to thefault compared to other metering devices also subject to the outage(e.g., the same outage as the metering device). The relative distancecan refer to the distance of one or more metering devices experiencingthe power outages relative to the location of the fault.

Still referring to FIG. 3 , the data processing system 202 can computeand identify the characteristics of the fault, such as based onsimulated fault characteristics in voltages and current waveforms. Forexample, the voltage waveforms and corresponding root mean square(“RMS”) signals as a function of time that can be measured by themetering devices of the utility grid 100. The data processing system 202can include, be a part of, or correspond to the metering devicesmeasuring the waveform or RMS signals, for example. The voltage waveformcan be shown in graph 302.

The data processing system 202 can measure the voltage waveforms ingraph 302 from various metering devices, such as metering devices placedor located upstream, at, or downstream of a fault. In this case, thefault can be a simulated single line-to-ground (“SLG”) fault. Forexample, the waveform or signal 306 can correspond to at least onemetering device upstream of the fault. The waveform or signal 308 cancorrespond to at least one metering device at the fault. The waveform orsignal 310 can correspond to at least one metering device downstream ofthe fault. A downstream metering device can refer to the metering devicefurther from the substation 104. The upstream metering device may referto a metering device closer to the substation 104.

The data processing system 202 (or the server 204) can calculate RMSsignals of graph 304 corresponding to the waveforms of graph 302. TheRMS signal may lag the waveform by one-half cycle, such as due to theRMS value being calculated every half cycle. In some cases, the dataprocessing system 202 may calculate the RMS signal at other cycles, suchas more or less than one-half cycle. The subplot or graph 304representing the RMS signals as a function of time can include anindication of a point in time at which the RMS voltage drops below athreshold of its nominal value (e.g., 80%, 0.8 per unit (“pu”), 120Vrms, among other predetermined threshold set by the administrator oroperator of the data processing system 202 or the utility grid 100).This point in time where the RMS voltage drops below the threshold canbe referred to as a Power Outage Notification (“PON”) start, presentedas point(s) 312. The points 312 or PON start can be at the same Vrmsvalue. In some cases, the PON start may be at different time periods.Further, the subplot can include an indication of a predetermined amountof time (e.g., configurable by the administrator or operator of the dataprocessing system 202) after the voltage is sustained below 0.8 pu wherethe PON is triggered and sent to the data processing system 202. Thepoint after the predetermined amount of time can be referred to orlabeled as PON end, presented as point(s) 314. The subplots or graphs302 and 304 of FIG. 3 are examples of the simulated faultcharacteristics. As such, the voltage waveform characteristics maydiffer depending on the location of the metering device to the fault.

Referring to FIG. 4 , depicted is a graph 400 of example RMS voltagetime derivative (e.g., RMS voltage signal) behavior in the presence of athree-phase fault. In further example from the above, using RMS voltagesignal between 0.8 pu and the PON, the data processing system 202 candetermine or compute various statistical parameters (e.g., min, max,standard deviation, mean, etc.) from the first derivative in time of theRMS voltage signal (e.g., denoted as dV/dt) over data windows rangingfrom one sample in length to the entire data clip, such as from the PONstart to PON end. Graph 400 can include an example of the generalbehavior of the first derivative (e.g., RMS signal), such as for a3-phase fault that does not trip an upstream circuit breaker. The RMSsignal for a single-phase fault, or other faults that may or may nottrip the upstream circuit breaker may provide similar or varying signalbehavior. The data processing system 202 can apply or perform the one ormore operations or features discussed herein for any type of fault witheither the breaker being tripped or not tripped to determine the faultlocation, at least relative to one or more metering devices.

For example, the data processing system 202 can compute the relativedistance of a metering device to the fault by comparing the calculatedmetrics (e.g., the first derivative in time of the RMS voltage signal)to other metering devices within the utility grid 100 experiencing theoutage or metering devices that have sent PONs, such as to the dataprocessing system 202 (or headend system). The computed relativedistance can be shown in graph 400. Graph 400 can include an examplepattern or derivative behavior of various metering devices upstream ordownstream of the fault location 406. Line 402 can represent meteringdevices upstream from the fault location 406 and line 404 can representmetering devices downstream from the fault location 406. Upstreammetering devices may be closer to the substation 104 and downstreammetering devices can be further from the substation 104. For example,based on the derivatives computed at or around PON start time for themetering devices, the data processing system 202 may identify that thederivatives of upstream metering devices provide a shallow slope thatdoes not trend to zero. The data processing system 202 may identify thatthe derivatives of the downstream metering devices provide a steeperslope trending to zero, such as based on a comparison of line 404 toline 402. An example plot of the comparison between the metric and othermetering devices using a simulation of an SLG fault on phase B of a3-phase, unbalanced circuit can be shown in at least FIG. 5A. Otherpatterns in addition to the pattern of graph 400 can be shown in atleast FIGS. 5A-C.

Referring to FIG. 5A, depicted is a graph 500A of an example accuratesingle line-to-ground (“SLG”) fault localization on the phase the faultoccurred on. Each element (sometimes referred to as a tick, increment,or position) along the x-axis can represent a metering device on phase Bthat reported a PON to the data processing system 202 during an outage.The graph 500A (e.g., or other graphs 500B-C) can represent a normalizedtime series representing fault location likelihood. The computednormalized derivative in graph 500A can represent the phase B of athree-phase power line reporting the PON. In this case, at least forillustration purposes, the arrangement of the metering devices can bepresented from left-to-right as closest-to-farthest, respectively, fromthe substation 104. In some cases, the denoted L and M along the x-axismay represent the metering device and sample, respectively. In someother cases, the denoted L and M may represent a location, area, orgeneral position and the metering device at the location, respectively.For example, at L1, M1 (e.g., metering device one or first meteringdevice at L1) can be closer to the substation 104 than M2 (e.g.,metering device two or second metering device at L1). In anotherexample, L1 to L11 of graphs 500A-C can represent locations closest tofarthest from the substation 104.

In this example, the fault may occur between metering devices L6.M1 andL6.M2, such as indicated by the vertical line within the graphs 500A-C(e.g., fault location 502). A circuit breaker located at the substation104 may be tripped (e.g., removing power from all three phases) at arealistic time delay after the fault. The metering devices L1.M1 toL6.M1 may be upstream from the fault location 502 (e.g., upstreammetering devices 504). The metering devices L6.M2 to L11.M2 may bedownstream from the fault location 502 (e.g., downstream meteringdevices 506). Similar to the pattern of graph 400, the derivatives ofthe upstream metering devices 504 (e.g., located near the substation 104to the fault location 502) can exhibit a shallow incline behavior nottrending to zero. Further, the derivatives of the downstream meteringdevices 506 (e.g., from fault location 502 onward) can exhibit a steeperdecline (e.g., compared to the upstream metering devices 504) behaviortrending to zero.

For each metering device within the utility grid 100, the dataprocessing system 202 can calculate their respective metrics from thefirst derivative in time of the RMS voltage signal recorded during thefault (e.g., from 0.8 pu to the PON trigger, referring to the previousexample). The data processing system 202 can normalize the calculatedmetrics to 100, as plotted in FIGS. 5A-C, for example. Accordingly,using the above techniques or algorithms, the data processing system 202can locate a metering device or the two closest metering devices to thefault (e.g., L6.M1 and L6.M2). The asymmetric behavior, as shown inexample FIG. 5A, between the upstream metering devices 504 anddownstream metering devices 506, may be due to the latent voltage onphases A and C which may drop slower if the metering device is fartherdownstream from the substation 104 after the 3-phase circuit breaker istripped. In some cases, the data processing system 202 can compare thepattern shown in at least one of graph 400, graph 500A, etc. to agenerated time series, such as to determine the location of the faultrelative to one or more metering devices. In this example, the graph500A can show the locations of metering devices L1.M1 to L11.M2 relativeto the fault location 502.

Referring to FIG. 5B, an example graph 500B of an accurate SLG faultlocation among all three phases is shown. The fault depicted in graph500B can be similar to the fault of graph 500A. In this example, thedata processing system 202 can generate and normalize a time series forall phases (e.g., phase A, phase B, and phase C) of the three-power.Referring to the previous example of graph 500A, phase B may be shownwithout the non-faulted phases (e.g., phase A and phase C). For example,graph 500B can include the phase B line 510 similar to the time seriesbehavior of graph 500A. The computed time series for phase A and phase Ccan correspond to lines 508 and 512, respectively. As shown in thisexample, the magnitude at the fault location 502 of the determinedmetrics (e.g., phase B) is higher on the faulted phase as compared tothe non-faulted phases, such as phase A or phase C (e.g., indicatingapproximately 90% to 95% likelihood of fault location).

The non-faulted phases (e.g., phase A and phase C in this case) canexhibit different behaviors or patterns. For example, lines 508 and 512of graph 500B can indicate the behavior of phase A and phase C power,respectively, during an outage (e.g., at or around PON start time). Inthe example of graph 500B, the pattern of phase A (e.g., line 508) caninclude or indicate a slight incline slope from L1.M1 to L5.M2, and asteep decline slope from L6.M2 to L9.M1. In another example of graph500B, the pattern of phase B (e.g., line 512) can indicate a gradualinclination (e.g., small increases) from the upstream metering devices504 to the downstream metering devices 506. Other patterns may beexhibited by the non-faulted phases.

The data processing system 202 may compare the normalized time series ofat least one faulted phase to a predetermined pattern. In this example,the data processing system 202 can compare phase B to the pattern, suchas patterns shown in at least one of graphs 400, 500A, and 500B. In somecases, the data processing system 202 may compare one or more otherphases (e.g., faulted or non-faulted phases) to at least one additionalpattern. For instance, in an SLG fault, the data processing system 202can compare the three phases to three patterns associated with theindividual phases, such as shown in graph 500B. The predeterminedpatterns may indicate a historical location of a fault. In some cases,the data processing system 202 can determine or identify the faultlocation based on similarities between one or more phases of the timeseries compared to one or more respective patterns.

Referring now to FIG. 5C, an example graph 500C illustrating that thecomputed metrics can be used by the data processing system 202 tosuccessfully distinguish fault behavior as a function of the type offault and upstream circuit breaker operation. In particular, the dataprocessing system 202 can use the computed metrics compared to othermetering devices to distinguish fault behaviors as discussed herein. Thedata processing system 202 can calculate the three lines (e.g., lines510, 514, and 516) on the phase B signals at each metering device. TheSLG fault line with breaker trips of FIG. 5C (e.g., represented as line510) may correspond to phase B line as in FIGS. 5A-B.

The SLG fault line without breaker trips of FIG. 5C (e.g., representedas line 516) can be in the same condition (e.g., similar fault location502 or similar type of fault) as the SLG fault line with breaker trips,except that the upstream circuit breaker does not trip. In this case,the pattern of an SLG fault phase B without breaker trip may include orindicate a steeper incline which does not trend to 0 upstream of thefault location and a shallower decline that trend to 0 downstream of thefault location, for example. In the case of phase B without a breakertrip, the voltage may be maintained on one or more (or all three) phasesupstream of the fault location 502 (e.g., maintained for the upstreammetering devices 504). Accordingly, the metering devices downstream(e.g., downstream metering devices 506) of the fault location 502 can beseen to have a higher score (e.g., a high percentage of likelihood orrate of change) as they have lost power, while those upstream are stillconnected to the substation 104 and supplied with electricity since thebreaker did not trip in the case of line 516.

Further, the 3-phase fault line with no breaker trip in FIG. 5C (e.g.,represented as line 514) can illustrate the same condition (e.g., noupstream breaker trip, similar fault location 502, among others), butfor a 3-phase fault. In this example, due to the loss of voltage supportfrom the non-faulted phases, as seen in the SLG fault no breaker tripline (e.g., line 516), the scores associated with the downstreammetering devices 506 of line 514 may be higher compared to line 516, asthe voltage on the metering device drops faster, for example. Thevarious fault scenarios of graph 500C can be a subset of patternshistorically generated or captured by one or more metering devices orthe data processing system 202. Hence, the data processing system 202can associate or compare a generated time series or normalized timeseries to at least one pattern (e.g., indicating the past location(s) ofthe fault(s)) to identify the fault location 502.

As shown in FIGS. 5A-C, the data processing system 202 can compare thegenerated time series to one or more patterns (e.g., representingdifferent types of faults) to determine the fault location. In somecases, the data processing system 202 may determine that the faultlocation 502 is at a metering device or between two metering devices. Insome cases, the data processing system 202 may determine that the faultlocation 502 is at a peak of the time series or normalized time series.In some other cases, the data processing system 202 may determine thatthe fault location 502 may not be at the peak of the generated timeseries. For example, based on at least one of the time period of the RMSsignal used to calculate the derivative, type of fault, number offaulted phases, among other metrics, the predetermined patterns canindicate the behavior of the phase during the outage and the determinedlocation of the fault. Hence, the data processing system 202 can comparea generated time series to one or more patterns to determine the faultlocation 502 relative to various metering devices in the electricitydistribution grid.

Therefore, using the aforementioned techniques, the data processingsystem 202 can locate the epicenter of the outage among the meteringdevices that lost power or experienced outages. Thus, for an outage thataffects metering devices over a large geographical area, for example,the data processing system 202 can reduce the time to locate the faultand restore utility service to customers, thereby increasing customersatisfaction of the services and improving the CAIDI score for theutility grid 100. In some cases, if the metering devices affected by theoutage have accurate, high-resolution timestamp capability, the dataprocessing system 202 can receive the timestamps when the voltage firstfell below a threshold (e.g., 0.8 pu) from the metering devices, anddetermine the closest proximity to the fault indicated by the earliesttimestamps received from the metering devices.

Referring to FIG. 6 , an example flow diagram of a method 600 forlocating a fault within a utility grid. The example method 600 can beexecuted, performed, or otherwise carried out by one or more componentsof the utility grid 100 (e.g., computing device, metering devices 118,etc.), the system 200 (e.g., data processing system 202, server 204,etc.), or computing device 700. The method 600 can include monitoring acharacteristic of electricity, at ACT 602. At ACT 604, the method 600can include determining whether a drop in the characteristic ofelectricity is below a threshold. At ACT 606, the method 600 can includegenerating a time series. At ACT 608, the method 600 can includecomparing the time series with a predetermined pattern. At ACT 610, themethod 600 can include determining whether the time series matches apattern. At ACT 612, the method 600 can include determining a locationof the metering device relative to a location of a fault.

Still referring to FIG. 6 in further detail, at ACT 602, a meteringdevice (e.g., a data processing system) located on an electricitydistribution grid can monitor a characteristic of electricity. Thecharacteristic of electricity can include or refer to a waveform or RMSsignal. The characteristic of electricity may correspond to at least oneof a characteristic of voltage, current, power, etc. measured by themetering device. Other metering devices within the electricitydistribution grid may perform one or more features, functionalities, oroperations similar to the metering device, as discussed herein. Themetering devices within the electricity distribution grid cancommunicate or share information with each other, thereby triangulatinglocations of individual metering devices relative to a fault location(e.g., identify locations of the metering devices with respect to thefault).

At ACT 604, the metering device can determine, based on the monitoredcharacteristic of electricity, whether the characteristic of electricity(e.g., signal or waveform behavior) drops below a threshold. Thethreshold may be determined by the electrician, operator, oradministrator of the metering device, the data processing system, or theelectricity distribution grid, for example. In some cases, if themetering device does not detect the drop, the metering device cancontinue to monitor the electricity characteristic (e.g., return to ACT602).

The metering device may detect a drop in the characteristic ofelectricity below the threshold. The drop can indicate a fault on theelectricity distribution grid. For example, the metering device candetermine that the RMS signal drops and sustains below the threshold fora predetermined timeframe (e.g., 0.5 μs, 0.25 μs, etc.). The meteringdevice can detect the drop in response to the sustained signal below thethreshold for the predetermined timeframe.

For example, responsive to detecting the signal dropping below thethreshold, the metering device may start a PON timer (e.g., at PON starttime). Subsequent to the predetermined timeframe, the metering devicemay end the timer (e.g., at PON end time). In this example, the meteringdevice can detect the drop in the characteristic of electricity inresponse to the PON end. In some other cases, the metering device maydetect the drop in the characteristic of electricity in response to thesignal dropping below the threshold.

The metering device can trigger the PON (e.g., PON sending process) inresponse to detecting the drop in the characteristic of electricity. ThePON can include at least the metering device ID and timestamps. Themetering device (or other metering devices) can use the metering deviceID to obtain information associated with the respective metering deviceID, such as the measured waveform (e.g., voltage, current, etc.), RMSsignal, among others. The timestamps may include at least a time whenthe characteristic of electricity drops below the threshold, PON starttime, PON end time, etc. The metering device can send the PON to atleast one other metering device to analyze the electricitycharacteristic during the drop. In some cases, the metering device mayreceive one or more PONs from other metering devices to process thebehavior of the signal during the drop. In some cases, the meteringdevice may simultaneously receive and send PON(s) from or to othermetering devices.

At ACT 606, the metering device can generate a time series in responseor subsequent to detecting a drop in electricity below the threshold. Togenerate the time series, the metering device can determine or computethe rate of change (e.g., metric) of the characteristic of electricitybased on a derivative in time of RMS (e.g., RMS signal) of one of avoltage signal or a current signal, for example. The metering device candetermine the derivative of the RMS for various other metering devices.The metering device can generate the time series of the rate of changeof the characteristic of electricity for a predetermined number ofcycles subsequent to the detection of the drop. The predetermined numberof cycles may be preset or preconfigured by the operator of theelectricity distribution grid or the metering device, for example. Insome cases, the predetermined number of cycles can refer to the totalnumber of metering devices affected by the outage. For instance, themetering device can determine the derivatives at or around the time ofthe drop for the various metering devices to generate the time series.

In some cases, the predetermined number of cycles can refer to the timeduration or a number of waveform cycles after detecting the drop (e.g.,at PON start time). For instance, the metering device may collect thesignal behavior or computed rate of change from various metering devicesaffected by the fault for the predetermined time duration (e.g., untilthe respective PON end). The metering device can update the time serieswith the rate of change of the metering devices within the electricitydistribution grid. Accordingly, in response to the predetermined numberof cycles, the metering device can aggregate and generate the timeseries representative of the rate of change of the electricity behaviorduring an outage (e.g., at or around the PON start time) for themetering devices in the electricity distribution grid.

In some cases, the metering device can generate the time series for thepredetermined number of cycles in response to the triggered PON. Forexample, the metering device may trigger PON responsive to a timeout ofthe PON timer (e.g., electricity sustained below the threshold). Themetering device can receive, for the predetermined number of cycles,PONs from one or more other metering devices including at least one ofthe measured RMS, the rate of change, among others. Responsive toreceiving the PON, the metering device can generate and update the timeseries with data from other metering devices. In some cases, themetering device can send information to at least one other meteringdevice to generate the time series.

The time series can include a list or array of metering devices based onthe location of the metering device with respect to a substation. Forexample, the metering device can list the metering devices in the x-axisof the time series from closest to farthest from the substation. In thisexample, the y-axis of the time series can include or indicate themagnitude of the rate of change in the characteristic of electricity orRMS. In some other cases, the location of the metering devices may belisted from farthest to closest to the substation.

At ACT 608, the metering device can compare the time series with atleast one predetermined pattern. The predetermined pattern can includeat least one of an incline slope, decline slope, zero slope, amongothers. In some cases, a declination of the decline slope may be greaterthan an inclination of the incline slope. In some other cases, thedeclination may be less than the inclination. In some other cases, thedeclination and the inclination may increase or decrease at a similarrate. The pattern may indicate the rate of changes (or lack of changes)of the incline or decline at different locations in the time series. Themetering device can retrieve or obtain the predetermined pattern from alocal data storage, remote data storage, or cloud storage. The patternmay be based on historical time series generated by the metering deviceor other metering devices. The pattern can indicate the historical faultlocation relative to the locations of the metering devices in the timeseries, such as marked, confirmed, or approved by the electrician fixingthe power line or locating the fault.

At ACT 610, the metering device can determine whether the time seriesmatches at least one of the patterns. For example, the metering devicecan identify portions of the time series with a first slope not trendingto zero and a second slope trending to zero. The metering device cancompare the inclination rate (e.g., of the first slope) and thedeclination of rate (e.g., of the second slope) to the rate of change ofthe slope in the pattern. In some cases, the inclination rate may begreater than the declination rate from closest to farthest meteringdevices from the substation. In some other cases, the inclination ratemay be less than the declination rate. The pattern may indicate the typeof fault experienced by the metering devices. In some cases, the patternmay indicate the one or more phases (e.g., of a 3-phase power) with afault. Accordingly, the metering device can compare the behavior of therate of change in the generated time series to the pattern to determinewhether the two match.

If the pattern does not match, the metering device may continue tomonitor the characteristic of electricity (e.g., return to step 602) toobtain additional electricity characteristics or generate another timeseries, for example. In some cases, the metering device may obtainadditional patterns from cloud storage or local storage of anothermetering device for another comparison. If the pattern matches thebehavior of the rate of change in the time series, the metering devicecan proceed to step 612 to determine the location of the fault.

At step 612, the metering device can determine the location of themetering device on the electricity distribution grid relative to thelocation of the fault (e.g., fault location) on the electricitydistribution grid. The metering device can determine the relativelocation to the fault based on the comparison of the time series of therate of change with the predetermined pattern. For example, the meteringdevice can determine that the location of the metering device (e.g., themetering device performing the determination) is downstream a firstsubset of metering devices and upstream a second subset of meteringdevices based on the predetermined pattern. In this example, the inclineslope in the time series or the pattern may be associated with the firstsubset of metering devices upstream of the metering device location.Further, the decline slope of the time series or the pattern may beassociated with the second subset of metering devices downstream of themetering device location.

Based on the pattern, the metering device can detect that the faultlocation is at or near the metering device location. For instance, thepattern may indicate a fault location downstream the first subset ofmetering devices and upstream the second subset of metering devices. Inanother example, the pattern may indicate the fault location at oraround the peak of the time series. The pattern may indicate the faultlocation at other points within the behavior of the time series (e.g.,other metering devices locations). In some cases, the fault location maybe in between two metering devices. For instance, the metering devicecan identify a second metering device having a similar rate of change orRMS behavior that is downstream the first subset of metering devices andupstream the second subset of metering devices. Based on a comparisonwith the pattern, the metering device can determine that the faultlocation is between the metering device location and the second meteringdevice location.

The metering device can provide, via a network, an indication of thelocation of at least one of the second metering device or the faultlocation on the electricity distribution grid to a computing systemcomprising one or more processors coupled to memory. In some cases, themetering device can provide an indication of the metering devicelocation relative to the fault to the computing system. The computingsystem may be operated by an electrician, operator, or administrator.For instance, responsive to receiving the indication, the operator maytravel to the determined fault location to restore electricity to themetering devices (or metering devices downstream of the fault). In somecases, the metering device may provide the metering device locationrelative to the fault. In some other cases, based on the generated timeseries compared to the predetermined pattern, the metering device candetermine that the fault is located at or near the second meteringdevice (e.g., the second metering device). Accordingly, the meteringdevice can provide the indication of the location of the second meteringdevice (e.g., corresponding to the determined fault location) to thecomputing system of the operator.

In some cases, if the fault location is between a first metering deviceand a second metering device, the metering device may provide anindication of the location of the first metering device, the location ofthe second metering device, or both locations to the computing system.When providing the location of one metering device, such as a firstmetering device or a second metering device, the metering device mayindicate whether the fault is determined to be downstream or upstreamthe respective metering device. For instance, if the first meteringdevice is closer to the substation than the second metering device, andthe fault is between the two metering devices, the metering device canindicate that the fault is downstream the first metering device orupstream the second metering device.

Further from the above example, the computing system can receive varioustime series of rates of change generated by metering devices located onthe electricity distribution grid. The computing system can receive thetime series responsive to drops in characteristics of electricity, suchas experienced or detected by the individual metering devices. Thecomputing system may normalize the values of the time series (e.g.,normalize the rates of change) responsive to receiving the time series.The computing system can determine, based on the normalized values ofthe plurality of time series, a likelihood of fault location at each ofthe metering devices (e.g., each metering device location). For example,the normalized rates of changes of the various metering devices cancorrespond to or represent the likelihood of fault location representedas a percentage, value, character, grade (e.g., A-F), among otherratings. Based on the likelihood, the computing system can determinethat the fault is located upstream of at least a first metering deviceand downstream of at least a second metering device.

In some cases, the computing system may determine that the fault is nextto the substation (e.g., upstream the metering devices on theelectricity distribution grid) based on the likelihood. In some othercases, the computing system may determine that the fault is near or atthe end of the power line (e.g., downstream metering devices on theelectricity distribution grid, upstream the metering device farthestfrom the substation). In some cases, the computing system can correspondto, include, or be a part of the data processing system.

In some cases, the electricity distribution grid can include a secondmetering device located at a different location from the meteringdevice. The second metering device can perform one or more features orfunctionalities similar to the metering device or the data processingsystem to determine the location of the second metering device relativeto the fault location. For example, the second metering device candetect a second drop in the characteristic of electricity below thethreshold indicating the fault (e.g., the same fault detected by thefirst metering device) on the electricity distribution grid. The secondmetering device can generate a second time series of a second rate ofchange of the characteristic of electricity responsive to the seconddrop in the characteristic of electricity below the threshold. Thesecond metering device can generate the second time series for thepredetermined number of cycles subsequent to the detection of the seconddrop. The second metering device can compare the second time series tothe predetermined pattern (or another predetermined pattern). The secondmetering device can determine, based on the comparison (e.g., a secondcomparison) of the second time series of the second rate of change withthe predetermined pattern, a location of the second metering device onthe electricity distribution grid relative to the location of the faulton the electricity distribution grid. Accordingly, the second meteringdevice can provide an indication of the second metering device locationor the fault location to the computing system.

FIG. 7 is a block diagram of an example computer system 700. Thecomputer system or computing device 700 can include or be used toimplement the data processing system 202, or its components such as thedata processing system 202. The computing system 700 includes at leastone bus 705 or other communication component for communicatinginformation and at least one processor 710 or processing circuit coupledto the bus 705 for processing information. The computing system 700 canalso include one or more processors 710 or processing circuits coupledto the bus for processing information. The computing system 700 alsoincludes at least one main memory 715, such as a random access memory(RAM) or other dynamic storage device, coupled to the bus 705 forstoring information, and instructions to be executed by the processor710. The main memory 715 can also be used for storing positioninformation, utility grid data, command instructions, device statusinformation, environmental information within or external to the utilitygrid, information on characteristics of electricity, or otherinformation during execution of instructions by the processor 710. Thecomputing system 700 may further include at least one read only memory(ROM) 720 or other static storage device coupled to the bus 705 forstoring static information and instructions for the processor 710. Astorage device 725, such as a solid state device, magnetic disk oroptical disk, can be coupled to the bus 705 to persistently storeinformation and instructions.

The computing system 700 may be coupled via the bus 705 to a display765, such as a liquid crystal display, or active matrix display, fordisplaying information to a user such as an administrator of the dataprocessing system or the utility grid. An input device 760, such as akeyboard or voice interface may be coupled to the bus 705 forcommunicating information and commands to the processor 710. The inputdevice 760 can include a touch screen display 765. The input device 760can also include a cursor control, such as a mouse, a trackball, orcursor direction keys, for communicating direction information andcommand selections to the processor 710 and for controlling cursormovement on the display 765. The display 765 can be part of the dataprocessing system 202, or other component of FIG. 1 or FIG. 2 .

The processes, systems and methods described herein can be implementedby the computing system 700 in response to the processor 710 executingan arrangement of instructions contained in main memory 715. Suchinstructions can be read into main memory 715 from anothercomputer-readable medium, such as the storage device 725. Execution ofthe arrangement of instructions contained in main memory 715 causes thecomputing system 700 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory715. Hard-wired circuitry can be used in place of or in combination withsoftware instructions together with the systems and methods describedherein. Systems and methods described herein are not limited to anyspecific combination of hardware circuitry and software.

Although an example computing system has been described in FIG. 7 , thesubject matter including the operations described in this specificationcan be implemented in other types of digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them.

Some of the descriptions herein emphasize the structural independence ofthe aspects of the system components (e.g., arbitration component) andillustrate one grouping of operations and responsibilities of thesesystem components. Other groupings that execute similar overalloperations are understood to be within the scope of the presentapplication. Modules can be implemented in hardware or as computerinstructions on a non-transient computer-readable storage medium, andmodules can be distributed across various hardware- or computer-basedcomponents.

The systems described above can provide multiple ones of any or each ofthose components and these components can be provided on either astandalone system or on multiple instantiation in a distributed system.In addition, the systems and methods described above can be provided asone or more computer-readable programs or executable instructionsembodied on or in one or more articles of manufacture. The article ofmanufacture can be cloud storage, a hard disk, a CD-ROM, a flash memorycard, a PROM, a RAM, a ROM, or a magnetic tape. In general, thecomputer-readable programs can be implemented in any programminglanguage, such as LISP, PERL, C, C++, C#, PROLOG, or in any byte codelanguage such as JAVA. The software programs or executable instructionscan be stored on or in one or more articles of manufacture as objectcode.

Example and non-limiting module implementation elements include sensorsproviding any value determined herein, sensors providing any value thatis a precursor to a value determined herein, datalink or networkhardware including communication chips, oscillating crystals,communication links, cables, twisted pair wiring, coaxial wiring,shielded wiring, transmitters, receivers, or transceivers, logiccircuits, hard-wired logic circuits, reconfigurable logic circuits in aparticular non-transient state configured according to the modulespecification, any actuator including at least an electrical, hydraulic,or pneumatic actuator, a solenoid, an op-amp, analog control elements(springs, filters, integrators, adders, dividers, gain elements), ordigital control elements.

The subject matter and the operations described in this specificationcan be implemented in digital electronic circuitry, or in computersoftware, firmware, or hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. The subject matter described in thisspecification can be implemented as one or more computer programs, e.g.,one or more circuits of computer program instructions, encoded on one ormore computer storage media for execution by, or to control theoperation of, data processing apparatuses. Alternatively or in addition,the program instructions can be encoded on an artificially generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. A computer storage medium can be, or be includedin, a computer-readable storage device, a computer-readable storagesubstrate, a random or serial access memory array or device, or acombination of one or more of them. While a computer storage medium isnot a propagated signal, a computer storage medium can be a source ordestination of computer program instructions encoded in an artificiallygenerated propagated signal. The computer storage medium can also be, orbe included in, one or more separate components or media (e.g., multipleCDs, disks, or other storage devices include cloud storage). Theoperations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The terms “computing device”, “component” or “data processing apparatus”or the like encompass various apparatuses, devices, and machines forprocessing data, including by way of example a programmable processor, acomputer, a system on a chip, or multiple ones, or combinations of theforegoing. The apparatus can include special purpose logic circuitry,e.g., an FPGA (field programmable gate array) or an ASIC (applicationspecific integrated circuit). The apparatus can also include, inaddition to hardware, code that creates an execution environment for thecomputer program in question, e.g., code that constitutes processorfirmware, a protocol stack, a database management system, an operatingsystem, a cross-platform runtime environment, a virtual machine, or acombination of one or more of them. The apparatus and executionenvironment can realize various different computing modelinfrastructures, such as web services, distributed computing and gridcomputing infrastructures.

A computer program (also known as a program, software, softwareapplication, app, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages,declarative or procedural languages, and can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, object, or other unit suitable for use in a computingenvironment. A computer program can correspond to a file in a filesystem. A computer program can be stored in a portion of a file thatholds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatuses can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit). Devices suitable for storingcomputer program instructions and data can include non-volatile memory,media and memory devices, including by way of example semiconductormemory devices, e.g., EPROM, EEPROM, and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto opticaldisks; and CD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

The subject matter described herein can be implemented in a computingsystem that includes a back end component, e.g., as a data server, orthat includes a middleware component, e.g., an application server, orthat includes a front end component, e.g., a client computer having agraphical user interface or a web browser through which a user caninteract with an implementation of the subject matter described in thisspecification, or a combination of one or more such back end,middleware, or front end components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

While operations are depicted in the drawings in a particular order,such operations are not required to be performed in the particular ordershown or in sequential order, and all illustrated operations are notrequired to be performed. Actions described herein can be performed in adifferent order.

Having now described some illustrative implementations, it is apparentthat the foregoing is illustrative and not limiting, having beenpresented by way of example. In particular, although many of theexamples presented herein involve specific combinations of method actsor system elements, those acts and those elements may be combined inother ways to accomplish the same objectives. Acts, elements andfeatures discussed in connection with one implementation are notintended to be excluded from a similar role in other implementations orimplementations.

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including” “comprising” “having” “containing” “involving”“characterized by” “characterized in that” and variations thereofherein, is meant to encompass the items listed thereafter, equivalentsthereof, and additional items, as well as alternate implementationsconsisting of the items listed thereafter exclusively. In oneimplementation, the systems and methods described herein consist of one,each combination of more than one, or all of the described elements,acts, or components.

Any references to implementations or elements or acts of the systems andmethods herein referred to in the singular may also embraceimplementations including a plurality of these elements, and anyreferences in plural to any implementation or element or act herein mayalso embrace implementations including only a single element. Referencesin the singular or plural form are not intended to limit the presentlydisclosed systems or methods, their components, acts, or elements tosingle or plural configurations. References to any act or element beingbased on any information, act or element may include implementationswhere the act or element is based at least in part on any information,act, or element.

Any implementation disclosed herein may be combined with any otherimplementation or embodiment, and references to “an implementation,”“some implementations,” “one implementation” or the like are notnecessarily mutually exclusive and are intended to indicate that aparticular feature, structure, or characteristic described in connectionwith the implementation may be included in at least one implementationor embodiment. Such terms as used herein are not necessarily allreferring to the same implementation. Any implementation may be combinedwith any other implementation, inclusively or exclusively, in any mannerconsistent with the aspects and implementations disclosed herein.

References to “or” may be construed as inclusive so that any termsdescribed using “or” may indicate any of a single, more than one, andall of the described terms. For example, a reference to “at least one of‘A’ and ‘B’” can include only ‘A’, only ‘B’, as well as both ‘A’ and‘B’. Such references used in conjunction with “comprising” or other openterminology can include additional items.

Where technical features in the drawings, detailed description or anyclaim are followed by reference signs, the reference signs have beenincluded to increase the intelligibility of the drawings, detaileddescription, and claims. Accordingly, neither the reference signs northeir absence have any limiting effect on the scope of any claimelements.

Modifications of described elements and acts such as variations insizes, dimensions, structures, shapes and proportions of the variouselements, values of parameters, mounting arrangements, use of materials,colors, orientations can occur without materially departing from theteachings and advantages of the subject matter disclosed herein. Forexample, elements shown as integrally formed can be constructed ofmultiple parts or elements, the position of elements can be reversed orotherwise varied, and the nature or number of discrete elements orpositions can be altered or varied. Other substitutions, modifications,changes and omissions can also be made in the design, operatingconditions and arrangement of the disclosed elements and operationswithout departing from the scope of the present disclosure.

The systems and methods described herein may be embodied in otherspecific forms without departing from the characteristics thereof. Scopeof the systems and methods described herein is thus indicated by theappended claims, rather than the foregoing description, and changes thatcome within the meaning and range of equivalency of the claims areembraced therein.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what can be claimed, but rather as descriptions offeatures specific to particular embodiments of particular aspects.Certain features described in this specification in the context ofseparate embodiments can also be implemented in combination in a singleembodiment. Conversely, various features described in the context of asingle embodiment can also be implemented in multiple embodimentsseparately or in any suitable subcombination. Moreover, althoughfeatures can be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination can in some cases be excised from the combination, and theclaimed combination can be directed to a subcombination or variation ofa subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingcan be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated in a single software product or packaged intomultiple software products.

Thus, particular embodiments of the subject matter have been described.In some cases, the actions recited in the claims can be performed in adifferent order and still achieve desirable results. In addition, theprocesses depicted in the accompanying figures do not necessarilyrequire the particular order shown, or sequential order, to achievedesirable results.

What is claimed is:
 1. A method, comprising: detecting, by a meteringdevice comprising memory and one or more processors and located on anelectricity distribution grid, a drop in electricity below a thresholdindicating a fault on the electricity distribution grid; generating, bythe metering device responsive to the drop in the electricity below thethreshold, a time series of a rate of change of the electricity for apredetermined number of cycles subsequent to the detection of the drop;and determining, by at least one of the metering device or a computingdevice in communication with the metering device, based on a comparisonof the time series of the rate of change with a predetermined pattern, alocation of the metering device on the electricity distribution gridrelative to a location of the fault on the electricity distributiongrid.
 2. The method of claim 1, wherein the drop in the electricitycorresponds to a drop in voltage measured by the metering device.
 3. Themethod of claim 1, wherein the drop in the electricity corresponds to adrop in current measured by the metering device.
 4. The method of claim1, comprising: triggering, by the metering device, responsive to thedetected drop in the electricity, a power outage notification (PON); andgenerating, by the metering device, responsive to the triggered PON, thetime series of the rate of change of the electricity for thepredetermined number of cycles.
 5. The method of claim 1, comprising:determining, by the metering device, the rate of change of theelectricity based on a derivative in time of a root mean square (RMS) ofone of a voltage signal or a current signal.
 6. The method of claim 1,wherein the predetermined pattern comprises at least an incline slopeand a decline slope, wherein a declination of the decline slope isgreater than an inclination of the incline slope.
 7. The method of claim6, comprising: determining, by the metering device, that the location ofthe metering device is downstream a first subset of metering devices andupstream a second subset of metering devices based on the predeterminedpattern, wherein the incline slope is associated with the first subsetof metering devices upstream of the location of the metering device, andwherein the decline slope is associated with the second subset ofmetering devices downstream of the location of the metering device. 8.The method of claim 1, wherein the electricity distribution gridcomprises a second metering device located at a different location fromthe metering device, comprising: detecting, by the second meteringdevice, a second drop in the electricity below the threshold indicatingthe fault on the electricity distribution grid; generating, by thesecond metering device responsive to the second drop in the electricitybelow the threshold, a second time series of a second rate of change ofthe electricity for the predetermined number of cycles subsequent to thedetection of the second drop; and determining, by the second meteringdevice, based on a second comparison of the second time series of thesecond rate of change with the predetermined pattern, a location of thesecond metering device on the electricity distribution grid relative tothe location of the fault on the electricity distribution grid.
 9. Themethod of claim 8, comprising: providing, by the metering device, via anetwork, an indication of the location of at least one of the secondmetering device or the fault on the electricity distribution grid to acomputing system comprising a second one or more processors coupled to asecond memory.
 10. The method of claim 1, comprising: receiving, by acomputing system, a plurality of time series of rates of changegenerated by a plurality of metering devices located on the electricitydistribution grid responsive to drops in electricity; normalizing, bythe computing system, values of the plurality of time series;determining, by the computing system, based on the normalized values ofthe plurality of time series, a likelihood of fault location at each ofthe plurality of metering devices; and determining, by the computingsystem, that the fault is upstream of a first metering device anddownstream of a second metering device based on the likelihood.
 11. Asystem, comprising: a metering device located on an electricitydistribution grid, the metering device comprising one or more processorsand memory to: detect a drop in electricity below a threshold indicatinga fault on the electricity distribution grid; generate, responsive tothe drop in the electricity below the threshold, a time series of a rateof change of the electricity for a predetermined number of cyclessubsequent to the detection of the drop; and determine, based on acomparison of the time series of the rate of change with a predeterminedpattern, a location of the metering device on the electricitydistribution grid relative to a location of the fault on the electricitydistribution grid.
 12. The system of claim 11, wherein the drop in theelectricity corresponds to a drop in voltage measured by the meteringdevice.
 13. The system of claim 11, wherein the drop in the electricitycorresponds to a drop in current measured by the metering device. 14.The system of claim 11, wherein the one or more processors to: trigger,responsive to the detected drop in the electricity, a power outagenotification (PON); and generate, responsive to the triggered PON, thetime series of the rate of change of the electricity for thepredetermined number of cycles.
 15. The system of claim 11, wherein theone or more processors to: determine, the rate of change of theelectricity based on a derivative in time of a root mean square (RMS) ofone of a voltage signal or a current signal.
 16. The system of claim 11,wherein the predetermined pattern comprises at least an incline slopeand a decline slope, wherein a declination of the decline slope isgreater than an inclination of the incline slope.
 17. The system ofclaim 16, wherein the one or more processors to: determine that thelocation of the metering device is downstream a first subset of meteringdevices and upstream a second subset of metering devices based on thepredetermined pattern, wherein the incline slope is associated with thefirst subset of metering devices upstream of the location of themetering device, and wherein the decline slope is associated with asecond metering device downstream of the location of the meteringdevice.
 18. The system of claim 11, wherein the electricity distributiongrid comprises a second metering device located at a different locationfrom the metering device, the second metering device configured to:detect a second drop in the electricity below the threshold indicatingthe fault on the electricity distribution grid; generate, responsive tothe second drop in the electricity below the threshold, a second timeseries of a second rate of change of the electricity for thepredetermined number of cycles subsequent to the detection of the seconddrop; and determine, based on a second comparison of the second timeseries of the second rate of change with the predetermined pattern, alocation of the second metering device on the electricity distributiongrid relative to the location of the fault on the electricitydistribution grid.
 19. A non-transitory computer readable storage mediumstoring instructions that, when executed by one or more processors of acomputing system, cause the one or more processors to: receive aplurality of time series of rates of change generated by a plurality ofmetering devices located on an electricity distribution grid responsiveto drops in electricity; normalize values of the plurality of timeseries; determine, based on the normalized values of the plurality oftime series, a likelihood of fault location at each of the plurality ofmetering devices; determine that a fault is upstream of a first meteringdevice and downstream of a second metering device based on thelikelihood; and provide, responsive to the determination, an indicationof a location of the fault upstream of the first metering device anddownstream of the second metering device.
 20. The non-transitorycomputer readable storage medium of claim 19, wherein to provide theindication comprises instructions to provide the indication of thelocation of the fault to a device remote from the computing system tofacilitate repair of the fault.